{"id":2200,"date":"2023-08-28T13:03:12","date_gmt":"2023-08-28T17:03:12","guid":{"rendered":"https:\/\/research.ece.ncsu.edu\/enbisys\/?page_id=2200"},"modified":"2026-05-08T15:26:32","modified_gmt":"2026-05-08T19:26:32","slug":"past-research-projects","status":"publish","type":"page","link":"https:\/\/research.ece.ncsu.edu\/enbisys\/past-research-projects\/","title":{"rendered":"Past Research Projects"},"content":{"rendered":"\n\n\n\n\n

Below is a list of some of the previous research projects from the EnBiSys Lab. This list is not comprehensive, but does highlight some of the more recently completed projects.<\/em><\/p>\n\n\n\n

Convergence Informatics and Data Access for the Science and Technologies for Phosphorus Sustainability (STEPS) Center <\/h2>\n\n\n\n

The STEPS Center<\/a> is an NSF Science and Technology Center focused on phosphorus sustainability through the integration of research across disciplines. Convergence informatics seeks to bridge the gaps between STEPS research projects using integrative data management coupled with data-driven models and ML-based approaches. The EnBiSys Lab developed knowledge graphs as a visual representation of STEPS data, capturing how data is connected across multiple scales and projects. The goal was to create a dynamic, user-friendly website for STEPS researchers to search, compare and visualize the data collected across STEPS projects and disciplines.<\/p>\n\n\n\n

Application of Deep Learning Algorithms for Multiple Environment Soybean Performance Predictions <\/h2>\n\n\n\n

In collaboration with BASF<\/a>, this project aimed to predict soybean yield production from genotype, phenotype, and environmental data. The EnBiSys lab used state-of-the-art deep learning algorithms to provide efficient and explainable prediction results for optimal planting solutions for BASF.<\/p>\n\n\n\n

Exploring Translation Mechanisms Through Upstream Open Reading Frames in Plants<\/h2>\n\n\n\n

The goal of this project was to analyze open reading frames (uORFs) within the 5\u2019 leader sequences and explore their impact on translation efficiency. The EnBiSys lab applied machine learning methods to characterize the properties associated with uORFs. It aimed to enhance our comprehension of the mechanisms influencing uORFs activity. The outcomes of this research guided decisions in genomic breeding. <\/p>\n\n\n\n

Systems-level Measurements of Biophysical Parameters in the Dorsal\/NF-kappaB Pathway<\/h2>\n\n\n\n

The overall objective of this project was to perform detailed measurements of local biophysical parameters and global morphogen gradient properties to build and constrain a predictive, computational model of the Dorsal\/NF-\u03baB gradient in the early Drosophila <\/em>embryo. The models generated as part of this project tested whether these models necessarily contain \u201csloppy parameters,\u201d (wide ranges of estimated parameters that fit the experimental data) and led to discovering additional aspects that will improve the model. Our research also included our general knowledge of the NF-\u03baB signaling module that can be found in animals from Cnidarians to humans. Our lab implemented computer vision and image processing techniques to quantify the spatiotemporal behavior of dorsal gradient and build predictive models using mathematical modeling of the parameters involved in this process. This research was performed in collaboration with the Reeves Lab<\/a> at Texas A&M Univeristy<\/a>.<\/p>\n\n\n\n

Identification of Translational Hormone-Response Gene Networks and Cis-Regulatory Elements<\/h2>\n\n\n\n

The goal of this project was to formulate a systems approach for characterizing transcriptional and translational networks in Arabidopsis thaliana<\/em> in response to ethylene and auxin disruption at the whole-genome scale. This work is being used to identify and characterize the signal integration hubs and CREs that differentiate transcriptional and translational control. The EnBiSys lab helped to develop molecular network inference approaches for this project and explored feature spaces and explanatory predictive models to identify elements when only a single dataset is available or appropriate to use.<\/p>\n\n\n\n

Natural Variation and Systems-level Properties of Gene Regulation in Drosophila<\/h2>\n\n\n\n

In collaboration with the Reeves Lab<\/a> at Texas A&M Univeristy<\/a>, this project aimed to use the natural variation that occurs in a panel of wild-caught fly lines to characterize the GRN responsible for precise anterior-posterior (AP) patterning the early Drosophila<\/em> embryo. The EnBiSys lab assisted in the characterization of the dynamics of the GAP gene transcriptional network responsible for AP patterning by developing computer vision approaches for feature extraction and non-linear systems modeling and analysis. <\/p>\n\n\n\n

Collaborative Research: Modeling the Regulatory Network of InsP6 Signaling in Plants<\/h2>\n\n\n\n

This project utilized kinetic modeling strategies in combination with model analysis approaches. It aimed to better understand how various isomers of inositol phosphates\u2014key signaling components that play a role in phosphate absorption\u2014convey signaling information leading to changes in phosphate utilization at different developmental stages in plants (seeds versus leaves). Our lab modeled the inositol phosphate metabolic pathway using non-linear systems of ordinary differential equations.<\/p>\n\n\n\n

Implementation and Analysis of Novel Real-time QRS Detection Algorithms<\/h2>\n\n\n\n

The goal of this project was to develop an algorithm for real-time detection of the QRS complex in electrocardiogram (ECG) signals. This algorithm was to be used in an implantable intra-aortic balloon pump, which is used to mechanically assist heart failure patients. This project was funded through NuPulse, Inc. Our lab developed the optimal detection algorithm to the sponsor.<\/p>\n\n\n\n

INSPIRE: Dynamic Regulatory Modeling of the Iron Deficiency Response in Arabidopsis Thaliana<\/h2>\n\n\n\n

The goal of this project was to better understand the mechanisms associated with stress response in the Arabidopsis thaliana<\/em> roots. The project integrated novel modeling techniques with high-speed computing architectures to identify and model previously uncharacterized regulatory components that control iron homeostasis in Arabidopsis<\/em> across multiple cell types. Our lab modeled the responsible network by developing the optimal inference algorithm.<\/p>\n\n\n\n

Modeling of Cellulose, Hemicellulose and Lignin-Carbohydrate Complex Formation and Regulation to Understand Plant Cell Wall Structure <\/h2>\n\n\n\n

The goal of this project was to improve our fundamental understanding of the plant secondary cell wall. The lab developed computational learning strategies that integrated genome-based information and mathematical modeling to identify fundamental relationships between cellulose\/hemicellulose regulation and bioenergy traits critical for efficient production of biofuel from plant biomass. Our lab also formulated the computational algorithms needed to establish relationships between transcripts and quantified bioenergy traits.<\/p>\n\n\n\n

Regulation and Modeling of Lignin Biosynthesis <\/h2>\n\n\n\n

The goal of this project was to integrate data obtained from P. trichocarpa<\/em> transgenics to build a predictive model of the regulation and biosynthesis of lignin, a phenylpropanoid polymer that serves as a significant barrier to cellulosic sources of ethanol. Our lab modeled the regulatory mechanisms.<\/p>\n","protected":false},"excerpt":{"rendered":"

Below is a list of some of the previous research projects from the EnBiSys Lab. This list is not comprehensive, but does highlight some of the more recently completed projects. Convergence Informatics and Data Access for the Science and Technologies for Phosphorus Sustainability (STEPS) Center The STEPS Center is an NSF Science and Technology Center…<\/p>\n","protected":false},"author":121,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"ncst_dynamicHeaderBlockName":"ncst\/default-header","ncst_dynamicHeaderData":"{}","ncst_content_audit_freq":"","ncst_content_audit_date":"","ncst_content_audit_display":false,"ncst_backToTopFlag":"","footnotes":""},"class_list":["post-2200","page","type-page","status-publish","hentry"],"acf":[],"yoast_head":"\nPast Research Projects - EnBiSys Lab<\/title>\n<meta name=\"description\" content=\"Explore some of the more recently completed research projects conducted by the EnBiSys Lab at NC State University.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/research.ece.ncsu.edu\/enbisys\/past-research-projects\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Past Research Projects - 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